DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Myungjoon | ko |
dc.contributor.author | Yeo, Byung Chul | ko |
dc.contributor.author | Park, Youngtae | ko |
dc.contributor.author | Lee, Hyuck Mo | ko |
dc.contributor.author | Han, Sang Soo | ko |
dc.contributor.author | Kim, Donghun | ko |
dc.date.accessioned | 2020-01-30T07:20:05Z | - |
dc.date.available | 2020-01-30T07:20:05Z | - |
dc.date.created | 2020-01-29 | - |
dc.date.created | 2020-01-29 | - |
dc.date.issued | 2020-01 | - |
dc.identifier.citation | CHEMISTRY OF MATERIALS, v.32, no.2, pp.709 - 720 | - |
dc.identifier.issn | 0897-4756 | - |
dc.identifier.uri | http://hdl.handle.net/10203/271919 | - |
dc.description.abstract | The development of catalysts for the electrochemical N2 reduction reaction (NRR) with a low limiting potential and high Faradaic efficiency is highly desirable but remains challenging. Here, to achieve acceleration, we develop and report a slab graph convolutional neural network (SGCNN), an accurate and flexible machine learning (ML) model that is suited for probing surface reactions in catalysis. With a self-accumulated database of 3040 surface calculations at the density-functional-theory (DFT) level, SGCNN predicted the binding energies, ranging over 8 eV, of five key adsorbates (H, N2, N2H, NH, NH2) related to NRR performance with a mean absolute error (MAE) of only 0.23 eV. SGCNN only requires the low-level inputs of elemental properties available in the periodic table of elements and connectivity information of constituent atoms; thus, accelerations can be realized. Via a combined process of SGCNN-driven predictions and DFT verifications, four novel catalysts in the L12 crystal space, including V3Ir(111), Tc3Hf(111), V3Ni(111), and Tc3Ta(111), are proposed as stable candidates that likely exhibit both a lower limiting potential and higher Faradaic efficiency in the NRR, relative to the reference Mo(110). The ML work combined with a statistical data analysis reveals that catalytic surfaces with an average d-orbital occupation between 4 and 6 could lower the limiting potential and potentially overcome the scaling relation in the NRR. | - |
dc.language | English | - |
dc.publisher | AMER CHEMICAL SOC | - |
dc.title | Artificial Intelligence to Accelerate the Discovery of N-2 Electroreduction Catalysts | - |
dc.type | Article | - |
dc.identifier.wosid | 000510530500008 | - |
dc.identifier.scopusid | 2-s2.0-85078295727 | - |
dc.type.rims | ART | - |
dc.citation.volume | 32 | - |
dc.citation.issue | 2 | - |
dc.citation.beginningpage | 709 | - |
dc.citation.endingpage | 720 | - |
dc.citation.publicationname | CHEMISTRY OF MATERIALS | - |
dc.identifier.doi | 10.1021/acs.chemmater.9b03686 | - |
dc.contributor.localauthor | Lee, Hyuck Mo | - |
dc.contributor.nonIdAuthor | Kim, Myungjoon | - |
dc.contributor.nonIdAuthor | Yeo, Byung Chul | - |
dc.contributor.nonIdAuthor | Han, Sang Soo | - |
dc.contributor.nonIdAuthor | Kim, Donghun | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordPlus | AMMONIA-SYNTHESIS | - |
dc.subject.keywordPlus | ELECTROCHEMICAL SYNTHESIS | - |
dc.subject.keywordPlus | ATMOSPHERIC-PRESSURE | - |
dc.subject.keywordPlus | NITROGEN REDUCTION | - |
dc.subject.keywordPlus | LOW-TEMPERATURE | - |
dc.subject.keywordPlus | CO2 REDUCTION | - |
dc.subject.keywordPlus | ADSORPTION | - |
dc.subject.keywordPlus | ELECTROCATALYSTS | - |
dc.subject.keywordPlus | SUPPRESSION | - |
dc.subject.keywordPlus | MONOLAYER | - |
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